Autonomous mobile robot global path planning: a prior information-based particle swarm optimization approach

The path planning of autonomous mobile robots (PPoAMR) is a very complex multi-constraint problem. The main goal is to find the shortest collision-free path from the starting point to the target point. By the fact that the PPoAMR problem has the prior knowledge that the straight path between the sta...

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Bibliographic Details
Published in:Control theory and technology 2023-05, Vol.21 (2), p.173-189
Main Authors: Jia, Lixin, Li, Jinjun, Ni, Hongjie, Zhang, Dan
Format: Article
Language:English
Subjects:
Algorithms
Collision avoidance
Complexity
Computational Intelligence
Control
Control and Systems Theory
Engineering
Fitness
Mathematical analysis
Mechatronics
Optimization
Particle swarm optimization
Path planning
Polynomials
Research Article
Robot dynamics
Robotics
Systems Theory
Trajectory optimization
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Summary:The path planning of autonomous mobile robots (PPoAMR) is a very complex multi-constraint problem. The main goal is to find the shortest collision-free path from the starting point to the target point. By the fact that the PPoAMR problem has the prior knowledge that the straight path between the starting point and the target point is the optimum solution when obstacles are not considered. This paper proposes a new path planning algorithm based on the prior knowledge of PPoAMR, which includes the fitness value calculation method and the prior knowledge particle swarm optimization (PKPSO) algorithm. The new fitness calculation method can preserve the information carried by each individual as much as possible by adding an adaptive coefficient. The PKPSO algorithm modifies the particle velocity update method by adding a prior particle calculated from the prior knowledge of PPoAMR and also implemented an elite retention strategy, which improves the local optima evasion capability. In addition, the quintic polynomial trajectory optimization approach is devised to generate a smooth path. Finally, some experimental comparisons with those state-of-the-arts are carried out to demonstrate the effectiveness of the proposed path planning algorithm.
ISSN:2095-6983
2198-0942
DOI:10.1007/s11768-023-00139-w